Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Guochang Ye"'
1
Akademický článek
Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients
Autor: Guochang Ye, Vignesh Balasubramanian, John K-J. Li, Mehmet Kaya
Publikováno v: IEEE Journal of Translational Engineering in Health and Medicine, Vol 10, Pp 1-8 (2022)
Structured Abstract—Objective: Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). De
Externí odkaz: https://doaj.org/article/28373d62eb6a4b5cbba7dc98d567ef7f
Zobrazit plný text záznamu
2
Akademický článek
Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models
Autor: Guochang Ye, Peshala Thibbotuwawa Gamage, Vignesh Balasubramanian, John K.-J. Li, Ersoy Subasi, Munevver Mine Subasi, Mehmet Kaya
Publikováno v: Applied Sciences, Vol 13, Iss 8, p 5191 (2023)
Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to pred
Externí odkaz: https://doaj.org/article/413d281c7a3641fa97a54c0096526426
Zobrazit plný text záznamu
3
Akademický článek
Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data
Autor: Guochang Ye, Mehmet Kaya
Publikováno v: Bioengineering, Vol 9, Iss 2, p 81 (2022)
Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segm
Externí odkaz: https://doaj.org/article/01eda1f63f004df8949ba435627196b6
Zobrazit plný text záznamu
4
Akademický článek
Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery
Autor: Pengfei Dong, Guochang Ye, Mehmet Kaya, Linxia Gu
Publikováno v: Applied Sciences, Vol 10, Iss 17, p 5820 (2020)
In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on opti
Externí odkaz: https://doaj.org/article/05884ad6b3544181bfe3277aa228147f
Zobrazit plný text záznamu
9
Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery
Autor: Mehmet Kaya, Linxia Gu, Pengfei Dong, Guochang Ye
Publikováno v: Applied Sciences
Volume 10
Issue 17
Applied Sciences, Vol 10, Iss 5820, p 5820 (2020)
In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on opti
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::743b2e34f9c3687814a4ee6dcdb330c8
Zobrazit plný text záznamu
10
3D tissue engineered micro-tumors for optical-based therapeutic screening platform
Autor: Sudipta Seal, Soumen Das, Ryan Bailey, Trevor Schmitt, Timothy S. Hannon, Michael B. Fenn, Joseph L. Spano, Guochang Ye, Mohamed Elmajdob, Eric M. Mason
Publikováno v: Dynamics and Fluctuations in Biomedical Photonics XIII.
Melanoma is an underserved area of cancer research, with little focus on studying the effects of tumor extracellular matrix (ECM) properties on melanoma tumor progression, metastasis, and treatment efficacy. We’ve developed a Raman spectral mapping
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::83560d7b21e50e9a9c81612c0744777a
https://doi.org/10.1117/12.2217678
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje